虚拟现实
计算机科学
模拟病
眼动
可视化
人机交互
数据可视化
多媒体
计算机视觉
视觉传达
计算机图形学(图像)
人工智能
作者
Jeonghaeng Lee,Woojae Kim,Chao Yang,Ping An,Sanghoon Lee
出处
期刊:IEEE Transactions on Visualization and Computer Graphics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
标识
DOI:10.1109/tvcg.2024.3447838
摘要
Most 360 virtual reality (VR) contents have been developed without considering that users could be affected by VR sickness. Accordingly, users' viewing safety has been steadily highlighted as a critical problem in the VR market. In this study, we investigate a novel VR sickness mitigation framework based on human visual characteristics for the rendered VR content. First, we build a large-scale 360 VR content database termed VRSP360 (VR Sickness and Presence 360) dedicated to the analysis of VR sickness and thoroughly conduct eye-tracking experiments to measure human perception. In the experiment, we observe that the users' gaze distribution is highly center-biased when they experience excessive VR sickness. From this observation, we design a foveated filtering framework that limits high-frequency textures in the peripheral view to mitigate VR sickness. Particularly, given the human visual system's (HVS) non-uniform resolution with respect to the fovea, we also adopt the foveation-based filtering method using the trade-off between sickness mitigation and presence conservation, which reduces any loss in perceptual quality despite the filtering. We further demonstrate that our framework can effectively compress visual information by applying foveated compression. In addition, we develop two metrics (visual texture index and perceptual information index) to measure the effective preservation of user-perceived information despite the filtration of peripheral vision textures by our proposed mitigation method. Through rigorous subjective evaluation on both original content and its VR-sickness-mitigated version, we demonstrate that the proposed framework successfully mitigates VR sickness with a reduction rate of ∼ 19% on the proposed dataset.
科研通智能强力驱动
Strongly Powered by AbleSci AI